Optical computing

Last updated

Optical or photonic computing uses photons produced by lasers or diodes for computation. For decades, photons have promised to allow a higher bandwidth than the electrons used in conventional computers.

Photon elementary particle or quantum of light

The photon is a type of elementary particle, the quantum of the electromagnetic field including electromagnetic radiation such as light and radio waves, and the force carrier for the electromagnetic force. The invariant mass of the photon is zero; it always moves at the speed of light within a vacuum.

Diode abstract electronic component with two terminals that allows current to flow in one direction

A diode is a two-terminal electronic component that conducts current primarily in one direction ; it has low resistance in one direction, and high resistance in the other. A diode vacuum tube or thermionic diode is a vacuum tube with two electrodes, a heated cathode and a plate, in which electrons can flow in only one direction, from cathode to plate. A semiconductor diode, the most commonly used type today, is a crystalline piece of semiconductor material with a p–n junction connected to two electrical terminals. Semiconductor diodes were the first semiconductor electronic devices. The discovery of asymmetric electrical conduction across the contact between a crystalline mineral and a metal was made by German physicist Ferdinand Braun in 1874. Today, most diodes are made of silicon, but other materials such as gallium arsenide and germanium are used.

Bandwidth (signal processing) difference between the upper and lower frequencies passed by a filter, communication channel, or signal spectrum

Bandwidth is the difference between the upper and lower frequencies in a continuous band of frequencies. It is typically measured in hertz, and depending on context, may specifically refer to passband bandwidth or baseband bandwidth. Passband bandwidth is the difference between the upper and lower cutoff frequencies of, for example, a band-pass filter, a communication channel, or a signal spectrum. Baseband bandwidth applies to a low-pass filter or baseband signal; the bandwidth is equal to its upper cutoff frequency.

Contents

Most research projects focus on replacing current computer components with optical equivalents, resulting in an optical digital computer system processing binary data. This approach appears to offer the best short-term prospects for commercial optical computing, since optical components could be integrated into traditional computers to produce an optical-electronic hybrid. However, optoelectronic devices lose 30% of their energy converting electronic energy into photons and back; this conversion also slows the transmission of messages. All-optical computers eliminate the need for optical-electrical-optical (OEO) conversions, thus lessening the need for electrical power. [1]

Binary data is data whose unit can take on only two possible states, traditionally labeled as 0 and 1 in accordance with the binary numeral system and Boolean algebra.

Application-specific devices, such as synthetic aperture radar (SAR) and optical correlators , have been designed to use the principles of optical computing. Correlators can be used, for example, to detect and track objects, [2] and to classify serial time-domain optical data. [3]

An optical correlator is a device for comparing two signals by utilising the Fourier transforming properties of a lens. It is commonly used in optics for target tracking and identification.

Optical components for binary digital computer

The fundamental building block of modern electronic computers is the transistor. To replace electronic components with optical ones, an equivalent optical transistor is required. This is achieved using materials with a non-linear refractive index. In particular, materials exist [4] where the intensity of incoming light affects the intensity of the light transmitted through the material in a similar manner to the current response of a bipolar transistor. Such an optical transistor [5] [6] can be used to create optical logic gates, [6] which in turn are assembled into the higher level components of the computer's CPU. These will be nonlinear optical crystals used to manipulate light beams into controlling other light beams.

Transistor Basic electronics component

A transistor is a semiconductor device used to amplify or switch electronic signals and electrical power. It is composed of semiconductor material usually with at least three terminals for connection to an external circuit. A voltage or current applied to one pair of the transistor's terminals controls the current through another pair of terminals. Because the controlled (output) power can be higher than the controlling (input) power, a transistor can amplify a signal. Today, some transistors are packaged individually, but many more are found embedded in integrated circuits.

An optical transistor, also known as an optical switch or a light valve, is a device that switches or amplifies optical signals. Light occurring on an optical transistor’s input changes the intensity of light emitted from the transistor’s output while output power is supplied by an additional optical source. Since the input signal intensity may be weaker than that of the source, an optical transistor amplifies the optical signal. The device is the optical analog of the electronic transistor that forms the basis of modern electronic devices. Optical transistors provide a means to control light using only light and has applications in optical computing and fiber-optic communication networks. Such technology has the potential to exceed the speed of electronics, while saving more power.

Like any computing system, an Optical computing system needs three things to function well:

  1. optical processor
  2. optical data transfer, e.g. Fiber optic cable
  3. optical storage, e.g. CD/DVD/bluray, etc.

Substituting electrical components will need data format conversion from photons to electrons, which will make the system slower.

Controversy

There are disagreements between researchers about the future capabilities of optical computers; whether or not they may be able to compete with semiconductor-based electronic computers in terms of speed, power consumption, cost, and size is an open question. Critics note that [7] real-world logic systems require "logic-level restoration, cascadability, fan-out and input–output isolation", all of which are currently provided by electronic transistors at low cost, low power, and high speed. For optical logic to be competitive beyond a few niche applications, major breakthroughs in non-linear optical device technology would be required, or perhaps a change in the nature of computing itself. [8] [9]

In digital electronics, the fan-out of a logic gate output is the number of gate inputs it can drive.

Misconceptions, challenges, and prospects

A significant challenge to optical computing is that computation is a nonlinear process in which multiple signals must interact. Light, which is an electromagnetic wave, can only interact with another electromagnetic wave in the presence of electrons in a material, [10] and the strength of this interaction is much weaker for electromagnetic waves, such as light, than for the electronic signals in a conventional computer. This may result in the processing elements for an optical computer requiring more power and larger dimensions than those for a conventional electronic computer using transistors.[ citation needed ]

A further misconception[ by whom? ] is that since light can travel much faster than the drift velocity of electrons, and at frequencies measured in THz, optical transistors should be capable of extremely high frequencies. However, any electromagnetic wave must obey the transform limit, and therefore the rate at which an optical transistor can respond to a signal is still limited by its spectral bandwidth. However, in fiber optic communications, practical limits such as dispersion often constrain channels to bandwidths of 10s of GHz, only slightly better than many silicon transistors. Obtaining dramatically faster operation than electronic transistors would therefore require practical methods of transmitting ultrashort pulses down highly dispersive waveguides.

Photonic logic

Realization of a photonic controlled-NOT gate for use in quantum computing Optical-NOT-gate.png
Realization of a photonic controlled-NOT gate for use in quantum computing

Photonic logic is the use of photons (light) in logic gates (NOT, AND, OR, NAND, NOR, XOR, XNOR). Switching is obtained using nonlinear optical effects when two or more signals are combined. [6]

Resonators are especially useful in photonic logic, since they allow a build-up of energy from constructive interference, thus enhancing optical nonlinear effects.

Other approaches that have been investigated include photonic logic at a molecular level, using photoluminescent chemicals. In a demonstration, Witlicki et al. performed logical operations using molecules and SERS. [11]

Unconventional approaches

Time delays optical computing

The basic idea is to delay light (or any other signal) in order to perform useful computations. [12] Of interest would be to solve NP-complete problems as those are difficult problems for the conventional computers.

There are 2 basic properties of light that are actually used in this approach:

When solving a problem with time-delays the following steps must be followed:

The first problem attacked in this way was the Hamiltonian path problem. [12]

The simplest one is the subset sum problem. [13] An optical device solving an instance with 4 numbers {a1, a2, a3, a4} is depicted below:

Optical device for solving the Subset sum problem.png

The light will enter in Start node. It will be divided into 2 (sub)rays of smaller intensity. These 2 rays will arrive into the second node at moments a1 and 0. Each of them will be divided into 2 subrays which will arrive in the 3rd node at moments 0, a1, a2 and a1 + a2. These represents the all subsets of the set {a1, a2}. We expect fluctuations in the intensity of the signal at no more than 4 different moments. In the destination node we expect fluctuations at no more than 16 different moments (which are all the subsets of the given. If we have a fluctuation in the target moment B, it means that we have a solution of the problem, otherwise there is no subset whose sum of elements equals B. For the practical implementation we cannot have zero-length cables, thus all cables are increased with a small (fixed for all) value k. In this case the solution is expected at moment B+n*k.

Wavelength-based computing

Wavelength-based computing [14] can be used to solve the 3-SAT problem with n variables, m clause and with no more than 3 variables per clause. Each wavelength, contained in a light ray, is considered as possible value-assignments to n variables. The optical device contains prisms and mirrors are used to discriminate proper wavelengths which satisfy the formula.

Computing by xeroxing on transparencies

This approach uses a Xerox machine and transparent sheets for performing computations. [15] k-SAT problem with n variables, m clauses and at most k variables per clause has been solved in 3 steps:

Masking optical beams

The travelling salesman problem has been solved in [16] by using an optical approach. All possible TSP paths have been generated and stored in a binary matrix which was multiplied with another gray-scale vector containing the distances between cities. The multiplication is performed optically by using an optical correlator.

Optical Fourier co-processors

Many computations, particularly in scientific applications, require frequent use of the 2D discrete Fourier transform (DFT) – for example in solving differential equations describing propagation of waves or transfer of heat. Though modern GPU technologies typically enable high-speed computation of large 2D DFTs, techniques have been developed that can perform DFTs optically by utilising the natural Fourier transforming property of lenses. The input is encoded using a liquid crystal spatial light modulator and the result is measured using a conventional CMOS or CCD image sensor. Such optical architectures can offer superior scaling of computational complexity due to the inherently highly interconnected nature of optical propagation, and have been used to solve 2D heat equations. [17]

Ising machines

Physical computers whose design was inspired by the theoretical Ising model are called Ising machines. [18] [19] [20]

Yoshihisa Yamamoto pioneered building Ising machines using photons. Initially Yamamoto and his colleagues built an Ising machine using lasers, mirrors, and other optical components commonly found on an optical table. [18] [19]

Later a team at Hewlett Packard Labs including Dave Kielpinski developed photonic chip design tools and used them to build an Ising machine on a single chip, integrating 1,052 optical components on that single chip. [18]

See also

Related Research Articles

Distributed computing is a field of computer science that studies distributed systems. A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another. The components interact with one another in order to achieve a common goal. Three significant characteristics of distributed systems are: concurrency of components, lack of a global clock, and independent failure of components. Examples of distributed systems vary from SOA-based systems to massively multiplayer online games to peer-to-peer applications.

Quantum computing Using quantum-mechanical phenomena for computing

Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is used to perform such computation, which can be implemented theoretically or physically.

Photonics Branch of physics related to the technical applications of light

Photonics is the physical science of light (photon) generation, detection, and manipulation through emission, transmission, modulation, signal processing, switching, amplification, and sensing. Though covering all light's technical applications over the whole spectrum, most photonic applications are in the range of visible and near-infrared light. The term photonics developed as an outgrowth of the first practical semiconductor light emitters invented in the early 1960s and optical fibers developed in the 1970s.

This is a timeline of quantum computing.

Photonic crystal a periodic optical nanostructure that affects the motion of photons in much the same way that ionic lattices affect electrons in solids

A photonic crystal is a periodic optical nanostructure that affects the motion of photons in much the same way that ionic lattices affect electrons in solids. Photonic crystals occur in nature in the form of structural coloration and animal reflectors, and, in different forms, promise to be useful in a range of applications.

In quantum computing, a quantum algorithm is an algorithm which runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. A classical algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step or instruction can be performed on a classical computer. Similarly, a quantum algorithm is a step-by-step procedure, where each of the steps can be performed on a quantum computer. Although all classical algorithms can also be performed on a quantum computer, the term quantum algorithm is usually used for those algorithms which seem inherently quantum, or use some essential feature of quantum computation such as quantum superposition or quantum entanglement.

Finite-difference time-domain or Yee's method is a numerical analysis technique used for modeling computational electrodynamics. Since it is a time-domain method, FDTD solutions can cover a wide frequency range with a single simulation run, and treat nonlinear material properties in a natural way.

Quantum networks form an important element of quantum computing and quantum communication systems. Quantum networks facilitate the transmission of information in the form of quantum bits, also called qubits, between physically separated quantum processors. A quantum processor is a small quantum computer being able to perform quantum logic gates on a certain number of qubits. Quantum networks work in a similar way to classical networks. The main difference, as will be detailed more in later paragraphs, is that quantum networking like quantum computing is better at solving certain problems, such as modeling quantum systems.

Shlomi Dolev israeli computer scientist

Shlomi Dolev is a Rita Altura Trust Chair Professor in Computer Science at Ben-Gurion University of the Negev (BGU) and the head of the Frankel Center for Computer Science.

The interconnect bottleneck comprises limits on integrated circuit (IC) performance due to connections between components instead of their internal speed. In 2006 it was predicted to be a "looming crisis" by 2010.

A photonic integrated circuit (PIC) or integrated optical circuit is a device that integrates multiple photonic functions and as such is similar to an electronic integrated circuit. The major difference between the two is that a photonic integrated circuit provides functions for information signals imposed on optical wavelengths typically in the visible spectrum or near infrared 850 nm-1650 nm.

Silicon photonics The study and application of photonic systems which use silicon as an optical medium

Silicon photonics is the study and application of photonic systems which use silicon as an optical medium. The silicon is usually patterned with sub-micrometre precision, into microphotonic components. These operate in the infrared, most commonly at the 1.55 micrometre wavelength used by most fiber optic telecommunication systems. The silicon typically lies on top of a layer of silica in what is known as silicon on insulator (SOI).

Robert W. Boyd American physicist

Robert William Boyd is an American physicist noted for his work in optical physics and especially in nonlinear optics. He is currently Canada Excellence Research Chair Laureate in Quantum Nonlinear Optics at the University of Ottawa and on the Faculty at the University of Rochester.

Photonic molecules are a theoretical natural form of matter which can also be made artificially in which photons bind together to form "molecules". They were first theoretically predicted in 2007. Photonic molecules are formed when individual (massless) photons "interact with each other so strongly that they act as though they have mass". In an alternative definition, photons confined to two or more coupled optical cavities also reproduce the physics of interacting atomic energy levels, and have been termed as photonic molecules.

Linear Optical Quantum Computing or Linear Optics Quantum Computation (LOQC) is a paradigm of quantum computation, allowing universal quantum computation. LOQC uses photons as information carriers, mainly uses linear optical elements to process quantum information, and uses photon detectors and quantum memories to detect and store quantum information.

Boson sampling constitutes a restricted model of non-universal quantum computation introduced by S. Aaronson and A. Arkhipov. It consists of sampling from the probability distribution of identical bosons scattered by a linear interferometer. Although the problem is well defined for any bosonic particles, its photonic version is currently considered as the most promising platform for a scalable implementation of a boson sampling device, which makes it a non-universal approach to linear optical quantum computing. Moreover, while not universal, the boson sampling scheme is strongly believed to implement a classically hard task using far fewer physical resources than a full linear-optical quantum computing setup. This makes it an outstanding candidate for demonstrating the power of quantum computation in the near term.

Integrated quantum photonics, uses photonic integrated circuits to control photonic quantum states for applications in quantum technologies. As such, integrated quantum photonics provides a promising approach to the miniaturization and optical scaling of optical quantum circuits. Major areas of application of integrated quantum photonics include: quantum computing, quantum communication, quantum simulation, quantum walks and quantum metrology.

The KLM scheme or KLM protocol is an implementation of linear optical quantum computing (LOQC), developed in 2000 by Knill, Laflamme and Milburn. This protocol makes it possible to create universal quantum computers solely with linear optical tools. The KLM protocol uses linear optical elements, single photon sources and photon detectors as resources to construct a quantum computation scheme involving only ancilla resources, quantum teleportations and error corrections.

JCMsuite is a finite element analysis software package for the simulation and analysis of electromagnetic waves, elasticity and heat conduction. It also allows a mutual coupling between its optical, heat conduction and continuum mechanics solvers. The software is mainly applied for the analysis and optimization of nanooptical and microoptical systems. Its applications in research and development projects include dimensional metrology systems, photolithographic systems, photonic crystal fibers, VCSELs, Quantum-Dot emitters, light trapping in solar cells, and plasmonic systems. The design tasks can be embedded into the high-level scripting languages MATLAB and Python, enabling a scripting of design setups in order to define parameter dependent problems or to run parameter scans.

References

  1. Nolte, D.D. (2001). Mind at Light Speed: A New Kind of Intelligence. Simon and Schuster. p. 34. ISBN   978-0-7432-0501-6.
  2. Feitelson, Dror G. (1988). "Chapter 3: Optical Image and Signal Processing". Optical Computing: A Survey for Computer Scientists. Cambridge, Massachusetts: MIT Press. ISBN   978-0-262-06112-4.
  3. Kim, S. K.; Goda, K.; Fard, A. M.; Jalali, B. (2011). "Optical time-domain analog pattern correlator for high-speed real-time image recognition". Optics Letters. 36 (2): 220–2. Bibcode:2011OptL...36..220K. doi:10.1364/ol.36.000220. PMID   21263506.
  4. "Encyclopedia of Laser Physics and Technology - nonlinear index, Kerr effect".
  5. Jain, K.; Pratt, Jr., G. W. (1976). "Optical transistor". Appl. Phys. Lett. 28 (12): 719. Bibcode:1976ApPhL..28..719J. doi:10.1063/1.88627.
  6. 1 2 3 US 4382660,K. Jain&G.W. Pratt, Jr.,"Optical transistors and logic circuits embodying the same",published May 10, 1983
  7. Tucker, R.S. (2010). "The role of optics in computing". Nature Photonics. 4 (7): 405. Bibcode:2010NaPho...4..405T. doi:10.1038/nphoton.2010.162.
  8. Rajan, Renju; Babu, Padmanabhan Ramesh; Senthilnathan, Krishnamoorthy. "All-Optical Logic Gates Show Promise for Optical Computing". Photonics. Photonics Spectra. Retrieved 8 April 2018.
  9. Rajan, Renju; Babu, Padmanabhan Ramesh; Senthilnathan, Krishnamoorthy (2018). "The Dawn of Photonic Crystals: An Avenue for Optical Computing". Theoretical Foundations and Application of Photonic Crystals. Intech. doi:10.5772/intechopen.71253. ISBN   978-953-51-3962-1 . Retrieved 4 April 2018.
  10. Philip R. Wallace (1996). Paradox Lost: Images of the Quantum. ISBN   978-0387946597.
  11. Witlicki, Edward H.; Johnsen, Carsten; Hansen, Stinne W.; Silverstein, Daniel W.; Bottomley, Vincent J.; Jeppesen, Jan O.; Wong, Eric W.; Jensen, Lasse; Flood, Amar H. (2011). "Molecular Logic Gates Using Surface-Enhanced Raman-Scattered Light". J. Am. Chem. Soc. 133 (19): 7288–91. doi:10.1021/ja200992x. PMID   21510609.
  12. 1 2 Mihai Oltean (2006). A light-based device for solving the Hamiltonian path problem. Unconventional Computing. Springer LNCS 4135. pp. 217–227. arXiv: 0708.1496 . doi:10.1007/11839132_18.
  13. Mihai Oltean, Oana Muntean (2009). "Solving the subset-sum problem with a light-based device". Natural Computing. 8 (2): 321–331. arXiv: 0708.1964 . doi:10.1007/s11047-007-9059-3.
  14. Sama Goliaei, Saeed Jalili (2009). An Optical Wavelength-Based Solution to the 3-SAT Problem. Optical SuperComputing Workshop. pp. 77–85. Bibcode:2009LNCS.5882...77G. doi:10.1007/978-3-642-10442-8_10.
  15. Tom Head (2009). Parallel Computing by Xeroxing on Transparencies. Algorithmic Bioprocesses. Springer. pp. 631–637. doi:10.1007/978-3-540-88869-7_31.
  16. NT Shaked, S Messika, S Dolev, J Rosen (2007). "Optical solution for bounded NP-complete problems". Applied Optics. 46 (5): 711–724. Bibcode:2007ApOpt..46..711S. doi:10.1364/AO.46.000711.CS1 maint: multiple names: authors list (link)
  17. A. J. Macfaden, G. S. D. Gordon, T. D. Wilkinson (2017). "An optical Fourier transform coprocessor with direct phase determination". Scientific Reports. 7 (1): 13667. Bibcode:2017NatSR...713667M. doi:10.1038/s41598-017-13733-1. PMC   5651838 . PMID   29057903.CS1 maint: multiple names: authors list (link)
  18. 1 2 3 Rachel Courtland. "HPE's New Chip Marks a Milestone in Optical Computing".
  19. 1 2 Edwin Cartlidge. "New Ising-machine computers are taken for a spin".
  20. Adrian Cho. "Odd computer zips through knotty tasks".

Further reading